Joint analysis and estimation of stock prices and trading volume in Barndorff-Nielsen and Shephard stochastic volatility models

Economy – Quantitative Finance – Statistical Finance

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

26 pages, 16 figures

Scientific paper

We introduce a variant of the Barndorff-Nielsen and Shephard stochastic volatility model where the non Gaussian Ornstein-Uhlenbeck process describes some measure of trading intensity like trading volume or number of trades instead of unobservable instantaneous variance. We develop an explicit estimator based on martingale estimating functions in a bivariate model that is not a diffusion, but admits jumps. It is assumed that both the quantities are observed on a discrete grid of fixed width, and the observation horizon tends to infinity. We show that the estimator is consistent and asymptotically normal and give explicit expressions of the asymptotic covariance matrix. Our method is illustrated by a finite sample experiment and a statistical analysis on the International Business Machines Corporation (IBM) stock from the New York Stock Exchange (NYSE) and the Microsoft Corporation (MSFT) stock from Nasdaq during a history of five years.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Joint analysis and estimation of stock prices and trading volume in Barndorff-Nielsen and Shephard stochastic volatility models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Joint analysis and estimation of stock prices and trading volume in Barndorff-Nielsen and Shephard stochastic volatility models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Joint analysis and estimation of stock prices and trading volume in Barndorff-Nielsen and Shephard stochastic volatility models will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-436147

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.